It is hard to exaggerate the importance that fairness has now taken within Machine Learning (ML) [1]}, [2]}, [3]} (and references therein). ML being a data processing field, a common upstream source of biases leading to downstream discrimination is the data itself. A recent paper has extrapolated the max-entropy (max-ent) principle to debiasing the underlying data domain distribution itself [2]}. The approach, designed for binary domains, proceeds in two stages: it first modifies the data distribution to get a fair `seed' and then finds an approximation to this seed via a max-ent problem (or equivalently, a minimiser of the KL divergence) that meets domain constraints on aggregated statistics such as marginals.
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